Snow scene simulation method, system, electronic device and medium based on satellite images

Abstract

The present invention relates to a snow scene simulation method, system, electronic device and medium based on satellite images. The method comprises the steps of: obtaining vegetation areas in satellite images, processing them to obtain random snow scenes; obtaining building top contour areas in satellite images, processing them to obtain random snow scenes; obtaining road contour areas in satellite images, processing them to obtain random snow scenes; performing diffuse reflection adjustment on the obtained random snow scenes, thereby simulating winter snow scene effects. The method of the present invention requires low cost and does not require manual modeling by artists; randomization is performed based on actual urban buildings, roads and vegetation data, retaining most of the natural and cultural information of the city, and the snow scene coverage and range can be adjusted as needed, solving the problem that snow scene satellite images cannot be modified.

Description

Technical Field

[0001] The invention belongs to the field of vision of flight simulators, and in particular relates to a snow scene simulation method, system, electronic equipment and medium based on satellite images. Background Art

[0002] Winter snow scenes can cause huge changes in the color, appearance and clarity of landmarks around the airport, which has a significant impact on pilots' visual flight. Therefore, realistic simulation of winter snow scenes is required in simulators to facilitate pilots' training and help them adapt to the adverse effects of winter weather on flight.

[0003] Traditional winter snow scene modeling is generally done by: 1. Similar to the summer scene model, the artist separately builds a winter simulation scene model and switches the scene during the training process. This method can well control the scene fineness as needed, but the labor consumption is very large. Flight simulation is generally a large-scale simulation, and it is completely modeled manually by the artist, and the cost is unacceptable; 2. Use drones or satellites to take aerial photos of winter snow scenes, and directly use high-definition satellite images with winter snow scene information. This method has the most realistic effect, but the data collection process depends on actual meteorological conditions, and it is difficult to ensure that a large area has similar snow scenes at the same time. During the collection, factors such as time period, visibility, and atmospheric conditions may affect the collection results of snow scene satellite images, making it difficult to ensure the availability of the collected satellite images. Once the snow scene satellite images are collected, their snow coverage cannot be modified, and it is difficult to meet the requirements of training with different snow scene coverage levels; 3. Based on the summer scene model, snow scene information is randomly generated by computer. Although this method has the lowest cost, it is difficult for traditional random methods to simulate the impact of snow scenes on roads, roofs, and vegetation, making the snow scene effect too different from the real effect and failing to meet the requirements of simulator training.

[0004] The snow scene simulation process can be mainly divided into snowfall simulation and snow accumulation simulation according to the natural environment. Snowfall simulation is the environmental simulation of the process of snowflakes falling from the air. It mainly considers the interaction between snow particles, wind field and gravity. It has certain challenges to the generation technology and computing power. However, with the advancement of software engine technology and hardware solving ability, the realism of the snowfall environment has been greatly improved, and the technical level gap between various scene simulation research units is not very large. Snow accumulation simulation is the accumulation of snow when snow falls on the ground or other objects. It mainly needs to consider the influence of factors such as surface structure, landing point material, sunlight angle, atmospheric temperature, and altitude on snow accumulation. At present, the implementation methods and technical levels of various companies for the simulation of snow accumulation vary. Therefore, it is urgent to study a new snow scene simulation method. Summary of the invention

[0005] In order to overcome the problems existing in the prior art, the present invention provides a snow scene simulation method, system, electronic device and medium based on satellite images, which are used to overcome the current defects.

[0006] A snow scene simulation method based on satellite images, the method comprising the steps of:

[0007] S1. Obtain vegetation areas in satellite images and process them to obtain random snow scenes;

[0008] S2. Obtain the building top outline area in the satellite image and process it to obtain a random snow scene;

[0009] S3. Obtaining the road contour area in the satellite image, and processing it to obtain a random snow scene;

[0010] S4. Perform diffuse reflection adjustment on the random snow scene obtained in S1, S2 and S3, so as to achieve simulation of snow scene effect.

[0011] According to the above aspects and any possible implementation manner, there is further provided an implementation manner, wherein the vegetation area includes an area where trees, grass and / or cultivated land are located.

[0012] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S1 includes:

[0013] S11. Preprocessing the satellite image to sharpen vegetation pixel features and weaken non-vegetation pixel features;

[0014] S12. extracting the vegetation pixel features and training the vegetation pixel feature samples;

[0015] S13. Using a classifier to determine the probability that a pixel in the sample belongs to a specific category;

[0016] S14. discriminate each pixel in the sample to determine the vegetation distribution area;

[0017] S15. Using the calibrated vegetation terrain elevation data, the data of the vegetation distribution area is converted into data based on the geocentric WGS-84 coordinate system;

[0018] S16. Randomly cover the vegetation distribution area based on the data of the geocentric WGS-84 coordinate system with snow;

[0019] S17. Outputting a random snow accumulation image in the vegetation area in the simulator view.

[0020] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S2 specifically includes:

[0021] S21. Obtaining building outline information from the satellite image;

[0022] S22. Calculate according to the building outline information to obtain the top structure information of the building;

[0023] S23. Calculate the top structure information of the building to obtain the top height information of the building;

[0024] S24. Randomly covering the building area processed by S23 with snow;

[0025] S25. Outputting a random snow accumulation image of the building in the simulator scene.

[0026] According to the above aspects and any possible implementation, an implementation is further provided, further comprising: S3 specifically comprises:

[0027] S31. Obtaining road profile information from the satellite image;

[0028] S32. Using the calibrated road profile elevation data, the road profile data is converted into data based on the geocentric WGS-84 coordinate system;

[0029] S33. Randomly cover the road area with snow based on the data of the geocentric WGS-84 coordinate system;

[0030] S34. Outputting a random snow accumulation image of the road area in the simulator vision.

[0031] According to the aspects described above and any possible implementation method, an implementation method is further provided, wherein S4 specifically includes fusing the direct light source color, the ambient light source color and the reflected light source color, the material reflection color and the light source direction, and performing diffuse reflection processing on the random snow scene.

[0032] According to the above aspects and any possible implementation, an implementation is further provided, wherein the step S16 uses a snow accumulation equation to cover the vegetation distribution area with random snow scenes, and the equation is expressed as follows: in, represents the rate of change of snow depth over time; P represents the snowfall rate; M p It represents the melting rate of the contact surface between snow and plants; E represents the melting and evaporation rate of the snow surface.

[0033] The present invention also provides a snow scene simulation system based on satellite images, wherein the system implements the method described above and comprises the following modules:

[0034] The first acquisition module is used to acquire the vegetation area in the satellite image and process it to obtain a random snow scene;

[0035] The second acquisition module is used to obtain the building top contour area in the satellite image and process it to obtain a random snow scene;

[0036] The third acquisition module is used to obtain the road contour area in the satellite image and process it to obtain a random snow scene;

[0037] The adjustment module is used to perform diffuse adjustment on all the random snow scenes obtained, so as to realize the simulation of the snow scene effect.

[0038] The present invention further provides an electronic device, comprising:

[0039] A memory storing executable instructions;

[0040] A processor is used to execute the executable instructions in the memory to implement the method.

[0041] The present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method described.

[0042] Beneficial Effects of the Invention

[0043] Compared with the prior art, the present invention has the following beneficial effects: the method of the present invention simulates the snow accumulation process in real time, thereby enhancing the environmental realism of the flight simulation training process and improving the effect of simulation training. The snow accumulation simulation is divided into four parts, namely, the relationship between snow accumulation on vegetation, snow accumulation on buildings, snow accumulation on both sides of the road, and snow accumulation at different times and the environment is studied. Therefore, the method requires low cost and does not require manual modeling by artists; randomization is performed based on actual urban buildings, roads, and vegetation data, retaining most of the natural and cultural information of the city, solving the problem of a large gap between completely random effects and real effects; and the snow scene coverage and range can be adjusted as needed, solving the problem that snow scene satellite images cannot be modified. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic diagram of a training sample area selected from a satellite image in the present invention;

[0045] Figure 2 is a schematic diagram of a process of generating a random snow scene in a vegetation area according to the present invention;

[0046] Figure 3 A schematic diagram of a process of generating a random snow scene in the top area of ​​a building according to the present invention;

[0047] Figure 4 A schematic diagram of a process of generating a random snow scene in a road area according to the present invention;

[0048] Figure 5 A schematic diagram of the outline of the building of the present invention;

[0049] Figure 6 A schematic diagram of the relationship between the spatial rectangular coordinate system of the present invention and a certain building;

[0050] Figure 7 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0051] In order to better understand the technical solution of the present invention, the content of the present invention includes but is not limited to the specific implementation methods described below, and similar technologies and methods should be considered to be within the scope of protection of the present invention. In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0052] It should be clear that the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] like Figure 7 As shown, the present invention provides a snow scene simulation method based on satellite images, the method comprising the steps of:

[0054] S1. Obtain vegetation areas in satellite images and process them to obtain random snow scenes;

[0055] S2. Obtain the building top outline area in the satellite image and process it to obtain a random snow scene;

[0056] S3. Obtaining the road contour area in the satellite image, and processing it to obtain a random snow scene;

[0057] S4. Perform diffuse reflection adjustment on the random snow scene obtained in S1, S2 and S3, so as to achieve simulation of snow scene effect.

[0058] Preferably, the vegetation area includes an area where trees, grass and / or cultivated land are located.

[0059] Preferably, the S1 includes:

[0060] S11. Preprocessing the satellite image to sharpen vegetation pixel features and weaken non-vegetation pixel features;

[0061] S12. extracting the vegetation pixel features and training the vegetation pixel feature samples;

[0062] S13. Using a classifier to determine the probability that a pixel in the sample belongs to a specific category;

[0063] S14. discriminate each pixel in the sample to determine the vegetation distribution area;

[0064] S15. Using the calibrated vegetation terrain elevation data, the data of the vegetation distribution area is converted into data based on the geocentric WGS-84 coordinate system;

[0065] S16. Randomly cover the vegetation distribution area based on the data of the geocentric WGS-84 coordinate system with snow;

[0066] S17. Outputting a random snow accumulation image in the vegetation area in the simulator view.

[0067] Preferably, the S2 specifically includes:

[0068] S21. Obtaining building outline information from the satellite image;

[0069] S22. Calculate according to the building outline information to obtain the top structure information of the building;

[0070] S23. Calculate the top structure information of the building to obtain the top height information of the building;

[0071] S24. Randomly covering the building area processed by S23 with snow;

[0072] S25. Outputting a random snow accumulation image of the building in the simulator scene.

[0073] Preferably, it also includes: S3 specifically includes:

[0074] S31. Obtaining road profile information from the satellite image;

[0075] S32. Using the calibrated road profile elevation data, the road profile data is converted into data based on the geocentric WGS-84 coordinate system;

[0076] S33. Randomly cover the road area with snow based on the data of the geocentric WGS-84 coordinate system;

[0077] S34. Outputting a random snow accumulation image of the road area in the simulator vision.

[0078] Preferably, the step S4 specifically includes fusing the direct light source color, the ambient light source color, the reflected light source color, the material reflection color and the light source direction, and performing diffuse reflection processing on the random snow scene.

[0079] Specifically, the implementation process of the present invention is as follows:

[0080] Step 1: Obtain vegetation areas from satellite images and cover them with random snow scenes

[0081] like Figure 1 As shown in FIG. 1 , the vegetation area in the satellite image includes trees, grass and / or cultivated land. The present invention uses the supervised classification method in computer vision recognition technology to identify the vegetation area, obtains the vegetation area data of the satellite image, and then uses the mass balance snow accumulation equation to combine the vegetation area data and the vegetation elevation data to perform random snow coverage. The process is as follows: Figure 2 As shown, the specific steps are as follows:

[0082] The first step is to pre-process the high-definition satellite images (satellite images for short) to sharpen the features of vegetation pixels and weaken the features of non-vegetation pixels. Image sharpening is to highlight the edges and contours of objects on the image, or the features of certain linear target elements, so as to improve the accuracy of computer automatic recognition.

[0083] The second step is to extract vegetation pixel features and train vegetation pixel feature samples in satellite images. Specifically, various vegetation pixel features are manually extracted in the sample area, and pixel feature samples representing vegetation in satellite images are trained as vegetation samples extracted from satellite images.

[0084] The third step is to design a classifier, using the principle of supervised classification to have prior knowledge of the vegetation attributes in the satellite image sample area before classification, and then use the pixel feature samples as a basis to complete the division of similar vegetation pixel feature areas in the satellite image (i.e., vegetation coding data) through the classifier (i.e., discriminant function). Then the present invention selects the maximum likelihood algorithm and calculates the probability density function based on the spectral distribution of the data, thereby determining the probability that the pixels of the satellite image belong to a specific category.

[0085] The fourth step is to traverse each pixel in the satellite image by combining the pixel feature samples and the probability of a specific category to identify the area representing vegetation in the satellite image. The equation for maximum likelihood estimation of continuous random variables is as follows:

[0086]

[0087] In order to facilitate pixel discrimination, logarithm is taken:

[0088]

[0089] Among them, x i is the observed vegetation sample data, θ is the parameter of the pixel to be estimated in the satellite image, and L(θ) is the likelihood function of the parameter θ of the pixel to be estimated. The purpose of maximum likelihood estimation is to find the maximum value of ln[L(θ)] That is, the closest x i The distribution of values, the set of values ​​represents the vegetation distribution area on the satellite image, ln is the natural logarithm, p(x i |θ) represents the function of traversing the pixels of satellite images.

[0090] The fifth step is to output the vegetation area data in the satellite image, and combine it with the calibrated vegetation terrain elevation (DEM) data to convert the vegetation area data into information based on the geocentric WGS-84 coordinate system. DEM data is a digital simulation of the ground terrain (i.e., a digital expression of the terrain surface morphology) through limited terrain elevation data. It uses a set of ordered numerical arrays to represent a physical ground model of ground elevation. It is a branch of the Digital Terrain Model (DTM), and other various terrain feature values ​​can be derived from it. Each pixel of the satellite image carries longitude and latitude coordinate data. These data are based on the geocentric WGS-84 coordinate system, but lack altitude values. This step assigns altitude values ​​to each satellite image pixel, so that the precise position of the vegetation area in the virtual world can be obtained, which is the basis for the snow on the vegetation surface in the simulator view;

[0091] The sixth step is to design a snow generation algorithm. After obtaining the high-definition satellite vegetation area with height values ​​in the fifth step, this step combines the calibrated high-level data with the snow accumulation equation to cover it with random snow scenes. The high-definition satellite vegetation area is the precise position of the vegetation in the simulator's vision. This position is the basis for snow accumulation on the vegetation surface. At this time, it is necessary to consider not only the influence of natural factors such as temperature, snowfall, and time on the depth and density of snow, but also the energy balance and mass balance principles in physics. The following snow accumulation equation reflects the energy and mass balance on both sides. The snow accumulation equation is expressed as follows:

[0092]

[0093] in, It represents the rate of change of snow depth over time. The simulator simulates the real-time natural world; P represents the snowfall rate; M p It represents the snowmelt rate at the contact surface between snow and plants (runoff rate at the bottom of snowpack); E represents the melting and evaporation rate of the snow surface.

[0094] Step 7: Output the snow image of the vegetation area in the simulator view.

[0095] The fifth step of the present invention obtains the position of vegetation in the simulator vision, the sixth step obtains the snow generation algorithm, or can be understood as a snow accumulation tool, and the seventh step combines the results of the fifth and sixth steps to output the snow in the vegetation area.

[0096] Step 2: Extract the top contour information of the building and the corresponding terrain height information, and cover it with random snow scenes

[0097] Extract building area information. The method of processing vegetation areas in step 1 can also be used for supervised classification extraction, but the accuracy is difficult to guarantee, especially for landmark buildings. Pilots need to use landmark buildings as key reference objects when flying. The present invention uses Shapefiles with higher accuracy to extract building outline data, and uses the outline data combined with DEM data to traverse the building height information. The building top information is calculated in the geocentric coordinate system established with terrain elevation data (DEM), and finally the snow accumulation equation algorithm is used for random snow coverage. The steps are as follows: Figure 3 As shown, specifically:

[0098] The first step is to obtain the building outline information. Shapefile files are used to describe the outline information and attributes of geometric objects. Shapefile files are a type of vector graphics format. The building outline information obtained using them is more accurate. This format includes points, polylines, and polygons. For example, Shapefile files can store the geometric positions of spatial objects such as buildings, rivers, and roads. These geometric positions are expressed in longitude and latitude. Therefore, the geometric shapes of the footprints of buildings, rivers, roads, etc. can be directly obtained. That is, by reading Shapefiles files, the required building outline information can be obtained, such as Figure 5 The coordinate information of the bottom corner points of the building shown, etc.

[0099] The second step is to obtain the building top structure information. After the building outline information has been obtained in the first step, the building outline information is combined with the elevation (DEM) data to obtain the terrain height corresponding to each point of the corresponding building. The building outline points are traversed in turn to calculate the building structure information within the building outline, that is, the building top structure information.

[0100] The third step is to calculate the building top height information. At present, the open source DEM product height value provides the orthogonal height (orthogonal height is the vertical distance from the measuring point to the geoid, and the geoid is similar to the local sea level). Therefore, the height value in the building top structure information in the second step is not the actual height of the building in the visual field, but only represents the building structure drop. To obtain the true building height value, it is necessary to correct the DEM data by the coordinate system. Since the earth itself is not a standard sphere, the sea level heights at different latitudes are not consistent, so it is necessary to establish the geocentric WGS-84 coordinate system to accurately describe the building height. The present invention solves the height after the correction of a certain point of the corresponding building by the simultaneous equations of the spatial rectangular coordinate system and the WGS-84 coordinate system.

[0101] Taking Building A as an example, according to Figure 5 The coordinates of the bottom corners of the building shown can be used to calculate the point P representing the theoretical terrain height of building A. A Each building has only one unique benchmark elevation position. P is obtained by using equations 4 and 5. AThe WGS-84 coordinates are (Lon(A), Lat(A)):

[0102]

[0103]

[0104] Where Lon(A) represents point P representing building A A Lat(A) represents point P of building A. A Latitude; Lon max (A) represents the maximum longitude value within the outline of building A; Lon min (A) represents the minimum longitude value within the outline of building A; Lat max (A) represents the maximum latitude value within the outline of building A; Lat min (A) represents the minimum latitude value within the outline of building A.

[0105] like Figure 6 As shown in the figure, the ray from the center of the earth to the intersection of 0 degrees longitude and 0 degrees latitude is the x-axis; the ray from the center of the earth to the intersection of 90 degrees east longitude and 0 degrees latitude is the y-axis; the ray from the center of the earth to the intersection of 0 degrees longitude and 90 degrees north latitude is the z-axis. By establishing a spatial rectangular coordinate system, point P can be calculated. A The coordinates in the rectangular coordinate system of space are recorded as (x A ,y A , z A ). Take point (x A ,y A , z A ) is the normal vector to make a plane. In the spatial rectangular coordinate system, the plane can be expressed by the following equation:

[0106] x A (xx A )+y A (yy A )+z A (zz A )=0

[0107] Formula 6

[0108] With the center of the earth 0 as the origin, make a line through P A The ray is recorded as L, and the point of the terrain height at sea level must be on L. By using a known algorithm, P can be calculated O The coordinates in the rectangular coordinate system of space are (x O ,y O , z O ). It can be seen that the ray L can be expressed in the spatial rectangular coordinate system as:

[0109]

[0110] By establishing the equation system through equation 6 and equation 7, the only solution of the equation system is:

[0111]

[0112] Obviously, this point (x, y, z) is the intersection of L and the building's base elevation plane, which is point P. A The corresponding corrected position can be used to calculate the height of each point on the top of the building according to its distance to the center of the earth.

[0113] The snow generation algorithm in the fourth step is the same as the snow algorithm in the sixth step of step one; the building snow output in the fifth step is the same as the seventh step of step one, and will not be described in detail here.

[0114] Step 3: Extract road contour information and corresponding terrain height information, and cover them with random snow scenes.

[0115] Extract road contour information. Use Shapefiles to extract road contour data in the same way as step 2. Combine the geocentric coordinate system established in step 2 to calculate the information on both sides of the road. Finally, use the snow accumulation equation algorithm to randomly cover the shoulders on both sides of the road with snow. When calculating the amount of snow, consider the road width factor, because the snow on both sides of the road is mainly the obvious snow formed by clearing the snow on the road. The process is as follows: Figure 4 As shown, no further details are given here.

[0116] Step 4: Adjust the diffuse reflection of the random snow scene according to the simulation process time, lighting angle, lighting color, etc., so as to achieve the simulation of the snow scene effect at different simulation times. The previous steps described the generation of snow in the right place. This step is to make the snow in different places or areas blend with the surrounding environment. Therefore, it is necessary to consider the lighting angle, lighting color, surrounding environment color, etc. of these different places to make the automatically generated snow more realistic.

[0117] According to the above steps, a large area of ​​snow is generated for vegetation, buildings, and roads. The formation and melting process of the snow scene in the visual scene is very rich, but the visual effect is not realistic enough. The reason is that the snow scene is not integrated into the atmospheric environment. The effect of the object seen by the human eye is mainly the diffuse reflection of the object itself in the environment. The diffuse reflection of the snow effect should also be considered. The main factors to be considered are the light color at different times (the simulator scene is a real-time virtual space, and different times refer to natural times such as morning, noon, and evening), the color of objects around the snow (ambient color), and the angle of light. The following expression is used to determine the color of the snow:

[0118]

[0119] In the formula, C LD Indicates the color of snow as seen by human eyes; C BLIndicates the color of the light source, which refers to the colors of various direct light sources such as the sun, moon, street lights, car lights, building lights, etc. at different time periods; C AL Indicates the color of the ambient light source, such as blue sky, red building, green billboard, etc.; C RL Indicates the color of the reflected light source, such as glass reflection, metal reflection, etc. Indicates the positive value of the dot product between the normal and the light source in the direction with the largest value; M D Represents the material reflection color of the snow itself.

[0120] As an embodiment disclosed by the present invention, the present invention further discloses a snow scene simulation system based on satellite images, wherein the system implements the method described, and comprises the following modules:

[0121] The first acquisition module is used to acquire the vegetation area in the satellite image and process it to obtain a random snow scene;

[0122] The second acquisition module is used to obtain the building top contour area in the satellite image and process it to obtain a random snow scene;

[0123] The third acquisition module is used to obtain the road contour area in the satellite image and process it to obtain a random snow scene;

[0124] The adjustment module is used to perform diffuse adjustment on all the random snow scenes obtained, so as to realize the simulation of the snow scene effect.

[0125] As an embodiment disclosed in the present invention, the present invention further discloses an electronic device, the electronic device comprising:

[0126] A memory storing executable instructions;

[0127] A processor is used to execute the executable instructions in the memory to implement the method described in the present invention.

[0128] As an embodiment disclosed in the present invention, the present invention further discloses a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method described in the present invention.

[0129] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0130] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed in the present invention, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the application concept of the present invention through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A winter snow scene simulation method based on satellite images, characterized in that: The method comprises the steps of: S1. Obtain vegetation areas in satellite images and process them to obtain random snow scenes, including: S11. Preprocessing the satellite image to sharpen vegetation pixel features and weaken non-vegetation pixel features; S12. extracting the vegetation pixel features and training the vegetation pixel feature samples; S13. Using a classifier to determine the probability that a pixel in the sample belongs to a specific category; S14. discriminate each pixel in the sample to determine the vegetation distribution area; S15. Using the calibrated vegetation terrain elevation data, the data of the vegetation distribution area is converted into data based on the geocentric WGS-84 coordinate system; S16. Randomly cover the vegetation distribution area based on the data of the geocentric WGS-84 coordinate system with snow; S17. Outputting a random snow accumulation image of the vegetation area in the simulator scene; S2. Obtain the building top outline area in the satellite image and process it to obtain a random snow scene, including: S21. Obtaining building outline information from the satellite image; S22. Calculate according to the building outline information to obtain the top structure information of the building; S23. Calculate the top structure information of the building to obtain the top height information of the building; S24. Randomly covering the building area processed by S23 with snow; S25. Outputting a random snow image of the building in the simulator scene; S3. Obtain the road contour area in the satellite image and process it to obtain a random snow scene, specifically including: S31. Obtaining road profile information from the satellite image; S32. Using the calibrated road profile elevation data, the road profile data is converted into data based on the geocentric WGS-84 coordinate system; S33. Randomly cover the road area with snow based on the data of the geocentric WGS-84 coordinate system; S34. Outputting a random snow image of the road area in the simulator vision; S4. Perform diffuse reflection adjustment on the random snow scene images obtained in S1, S2 and S3, so as to achieve simulation of winter snow scene effects.

2. The winter snow scene simulation method based on satellite images according to claim 1 is characterized in that: The vegetation area includes areas where trees, grass and / or cultivated land are located.

3. The winter snow scene simulation method based on satellite images according to claim 1 is characterized in that: The S4 specifically includes fusing the direct light source color, the ambient light source color, the reflected light source color, the material reflected color and the light source direction, and performing diffuse reflection processing on the random snow scene.

4. The winter snow scene simulation method based on satellite images according to claim 1 is characterized in that: The S16 uses a snow accumulation equation to cover the vegetation distribution area with random snow scenes, and the equation is expressed as follows: in, represents the rate of change of snow depth over time; P represents the snowfall rate; M p It represents the melting rate of the contact surface between snow and plants; E represents the melting and evaporation rate of the snow surface.

5. A winter snow scene simulation system based on satellite images, characterized in that: The system implements the method described in any one of claims 1 to 4, and includes the following modules: The first acquisition module is used to acquire the vegetation area in the satellite image and process it to obtain a random snow scene; The second acquisition module is used to obtain the building top contour area in the satellite image and process it to obtain a random snow scene; The third acquisition module is used to obtain the road contour area in the satellite image and process it to obtain a random snow scene; The adjustment module is used to perform diffuse adjustment on all the random snow scenes obtained, so as to realize the simulation of winter snow scene effects.

6. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the method according to any one of claims 1 to 4.

7. A computer storage medium, characterized in that: The medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 4.

Images